Commonality and Specificity of Acupuncture Point Selections.

OBJECTIVE: Because individual acupoints have a wide variety of indications, it is difficult to accurately identify the associations between acupoints and specific diseases. Thus, the present study aimed at revealing the commonality and specificity of acupoint selections using virtual medical diagnoses based on several cases. METHODS: Eighty currently practicing Korean Medicine doctors were asked to prescribe acupoints for virtual acupuncture treatment after being presented with medical information extracted from 10 case reports. The acupoints prescribed for each case were quantified; the data were normalised and compared among the 10 cases using z-scores. A hierarchical cluster analysis was conducted to categorise diseases treated based on the acupoint prescription patterns. Additionally, network analyses were performed on the acupoint prescriptions, at the individual case and cluster level. RESULTS: Acupoints ST36, LI4, and LR3 were most commonly prescribed across all diseases. Regarding the specific acupoints prescribed in each cluster, acupoints around the disease site (knee and lower back) were frequently used in cluster A (musculoskeletal symptoms), acupoints LI4, LR3, PC6, and KI3 were frequently used in cluster B (psychiatric symptoms), and acupoints ST36, LI4, LR3, PC6, CV12, and SP6 were frequently used in cluster C (several symptoms of diseases of internal medicine). CONCLUSIONS: The present study identified the commonality and specificity of acupoint selections based on virtual acupuncture treatments prescribed by practicing clinicians. Acupoint selection patterns, which were defined using a top-down approach in previous studies and classical medical texts, may be further elucidated using a bottom-up approach based on patient medical records.

Revealing Associations between Diagnosis Patterns and Acupoint Prescriptions Using Medical Data Extracted from Case Reports.

OBJECTIVE: The optimal acupoints for a particular disease can be determined by analysis of diagnosis patterns. The objective of this study was to reveal the association between such patterns and the acupoints prescribed in clinical practice using medical data extracted from case reports. METHODS: This study evaluated online virtual diagnoses made by currently practicing Korean medical doctors (N = 80). The doctors were presented with 10 case reports published in Korean medical journals and were asked to diagnose the patients and prescribe acupoints accordingly. A network analysis and the term frequency-inverse document frequency (tf-idf) method were used to analyse and quantify the relationship between diagnosis patterns and prescribed acupoints. RESULTS: The network analysis showed that ST36, LI4, LR3, and SP6 were the most frequently used acupoints across all diagnoses. The tf-idf values showed the acupoints used for specific diseases, such as BL40 for bladder disease and LU9 for lung disease. CONCLUSIONS: The associations between diagnosis patterns and prescribed acupoints were identified using an online virtual diagnosis modality. Network and text mining analyses revealed commonly applied and disease-specific acupoints in both qualitative and quantitative terms.